Grok 4 Fast now has 2M context window
docs.x.ai185 points by hereme888 2 days ago
185 points by hereme888 2 days ago
What matter is not context or the recod token/s you get.
But the quality for the model. And it seem Grok pushing the wrong metrics again, after launching fast.
I thought the number of tokens per second doesn't matter until I used Grok Code Fast. I realized that it makes a huge difference. If it take more than 30s to run, I lose focus, and look at something else. I end up being a lot less productive. It also opens up the possibility to automate a lot more simple tasks. I would def recommend people try fast models
If you are single tasking, speed matters to an extent. You need to still be able to read/skim the output and evaluate its quality.
The productive people I know use git worktrees and are multi-tasking.
The optimal workflow is when you can supply it one or more commands[1] that the model can run to validate/get feedback on its own. Think of it like RLHF for the LLM, they are getting feedback albeit not from you, which can be laborious.
As long as the model gets feedback it can run fairly autonomously with less supervision it does not have to testing driven feedback, if all it gets is you as the feedback, the bottleneck will be always be the human time to read, understand and evaluate the response not token speed.
With current leading models doing 3-4 workflows in parallel is not that hard, when fully concentrating, of course it is somewhat less when browsing HN :)
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[1] The command could be a unit test runner, or a build/compile step, or e2e workflows like for UI it could be Chrome MCP/CDP, playwright/cypress, or storybook-js and so on. There are even converts toversion of TDD to benefit from this gain.
You could have one built for your use case if no existing ones fit, with model help of course.
Hmm. I run maybe 3 work streams max in parallel and struggle to keep up with the context switching. I have some level of skepticism that your colleagues are amazingly better and do 4 and produce quality code at a faster rate than 1 or 2 work streams in wall clock time. I consider a workstream to be disparate features or bugs that are unrelated and require attention. Running 8 agents in parallel that are all doing the same thing is of course trivial nowadays but that in of itself is what I would consider a single threaded workstream.
We have similar definition of streams, but It depends on a lot of things from your tooling/ language , stack etc.
if your builds take a fair bit of time (incremental builds may not work in worktree first time) or you are working on a item that has high latency feedback like e2e suite that runs on a actual browser etc.
Prompt styles also influences this. I like to make fairly detailed prompt that cover a lot of the nuances upfront and spend 10-15 or more writing it. I find that when I do that it takes longer, but I only give simple feedback during the run itself freeing me to go next item. Some people prefer chat style approach, you cannot keep lot of threads in mind if chatting.
Model and cli client choice matters , on average codex is slower than sonnet 4.5 . Within each family if you enable thinking or use the high reasoning model it can be slower as well.
Finally not all tasks are equal, I like to mix some complex and simpler ones or add some dev ex or a refactor that requires lower attention budget with features that require more.
Having said that, while I don’t know 10x type developers. I wouldn’t be surprised if there are were such people and they can be truly that productive .
The analogy I think of is chess. Maybe I can play 2-3 games in parallel reasonably well, but there are professional players who can play dozens of games blindfolded and win all of them.
Nice answer - all of the above aligns with my experience.
I use sonnet a lot more than openai models and its speed means I do have to babysit it more and get chattier which does make a difference, probably you are right that if I was using codex which is on average 4-6 times slower than claude code that I would have more mental bandwidth to handle more workstreams.
Seems reductive. Some applications require higher context length or fast tokens/s. Consider it a multidimensional Pareto frontier you can optimize for.
It's not just that some absolutely require it, but a lot of applications hugely benefit from more context. A large part of LLM engineering for real world problems revolves around structuring the context and selectively providing the information needed while filtering out unneeded stuff. If you can just dump data into it without preprocessing, it saves a huge amount of development time.
Depending on the application, I think “without preprocessing” is a huge assumption here. LLMs typically do a terrible job of weighting poor quality context vs high quality context and filling an XL context with unstructured junk and expecting it to solve this for you is unlikely to end well.
In my own experience you quickly run into jarring tangents or “ghosts” of unrelated ideas that start to shape the main thread of consciousness and resist steering attempts.
It depends to the extent I already mentioned, but in the end more context always wins in my experience. If you for example want to provide a technical assistant, it works much better if you can provide an entire set of service manuals to the context instead of trying to put together relevant pieces via RAG.
Quality of the model tends to be pretty subjective, and people also complain about gaming benchmarks. At least context window length and generation speed are concrete improvements. There's always a way you can downplay how valuable or impressive a model is.
Depends. For coding at least, you can divide tasks into high-intelligence ($$$) and low-intelligence ($) tasks. Being able to do low-intelligence tasks super fast and cheap would be quite beneficial. A majority of code edits would fall into the fast-and-cheap subset.
Grok's biggest feature is that unlike all the other premier models (yes I know about ChatGPT's new adult mode), it hasn't been lobotomized by censoring.
I am amazed people actually believe this
Grok is the most biased of the lot, and they’re not even trying to hide it particularly well
Bias is not the same as censoring.
Censoring is "I'm afraid I can't let you do that, Dave".
Bias is "actually, Elon Musk waved to the crowd."
Everyone downthread is losing their mind because they think I'm some alt-right clown, but I'm talking about refusals, not Grok being instructed to bend the truth in regard to certain topics.
Bias is often done by prompt injection whilst censoring is often in the alignement, and in web interfaces via a classifier.
They are different, but they’re not that different.
If Grok doesn’t refuse to do something, but gives false information about it instead, that is both bias and censorship.
I agree that Grok gives the appearance of the least censored model. Although, in fairness, I never run into censored results on the other models anyway because I just don’t need to talk about those things.
According to a recent Economist article, even Grok is left-biased.
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Oh the hubris.
Relax downvoters, I write it pretty tongue-in-cheek understanding full well the scope of “real” political ideas, and think reasonable people can be all over the political spectrum.
This is quote seared into my head because my father says anything that disagrees with his conspiracies, it is a liberal bias. If I say “ivermectin doesn’t cure cancer”, that’s my liberal bias. “Climate change is not a hoax by the you-know-who’s to control the world” == liberal bias. “Bigfoot only exists in our imagination”… liberal bias (I’m not joking on any of these whatsoever).
So I’ve been saying this in my head and out loud to him for a looooong time.
Well, it's kind of a tautology, isn't it? Conservatism always loses in the end, for better or worse, simply because the world and everything in it undergoes change over time.
People believe it because they have eyes: https://nypost.com/2024/02/21/business/googles-ai-chatbot-ge...
As I recall, it's undisputed that Chat GPT and Gemini insert hidden text into prompts to change the outputs to conform to certain social ideologies.
> it's undisputed that Chat GPT and Gemini insert hidden text into prompts to change the outputs to conform to certain social ideologies
And why do you think Grok doesn’t? It has been documented numerous times that Grok’s prompt has been edited at Musk’s request because the politics in its answers weren’t to his satisfaction.
Nothing you posted (from an almost two year old article btw) in anyway refutes the prior comment.
Grok is significantly the most biased. Did you sleep through its continuous insertion of made up stuff about south africa?
This is the same person who is trying to re-write an entire encyclopedia because facts aren't biased enough.
A group has created an alternate reality echo chamber, and the more reality doesn't match up the more they are trying to invent a fake one.
When you're on the side of book banning and Orwellian re-writing of facts & history that side never turns out to have been the good side. It's human nature for some people to be drawn to it as an easy escape rather than allowing their world views to be challenged. But you'd be pretty pressed to find the group doing that any of the times it's been done to have been anything but a negative for their society.
It takes a lot of chutzpah to accuse people of "re-writing ... facts & history" while peddling AI (and movies and TV shows) that change the ethnicities of historical figures.
>> This is the same person who is trying to re-write an entire encyclopedia because facts aren't biased enough.
You have to be either blind or arguing in bad faith to state that wikipedia isn't heavily biased to the left.
Can’t help but feel everyone making a pro-Grok argument here isn’t actually making the case that it’s uncensored, rather that it’s censored in a way that aligns with their politics, and thus is good
It's almost always telling isn't it?
Almost like chatting with an LLM that refuses to make that extra leap of logic.
"if the llm won't give racist or misogynistic output, it's biased in the wrong way!"
I would argue over censorship is the better word. Ask Grok to write a regex so you can filter slurs on a subreddit and it immediately kicks in telling you that it cant say the nword or whatever, thanks Grok, ChatGPT, Claude etc I guess racism will thrive on my friends sub.
I can’t tell if this is serious or not. Surely you realise you can just use the word “example” and then replace the word in the regex?!
I think they would want a more optimized regex. Like a long list of swears, merged down into one pattern separated by tunnel characters, and with all common prefixes / suffixes combined for each group. That takes more than just replacing one word. Something like the output of the list-to-tree rust crate.
Wouldn't the best approach for that be to write a program that takes a list of words and output an optimized regex?
I'm sure an LLM can help write such a program. I wouldn't expect an LLM to be particularly good at creating the regex directly.
I would agree. That’s exactly what the example I gave (list-to-tree) does. LLMs are actually pretty OK at writing regexes, but for long word lists with prefix/suffix combinations they aren’t great I think. But I was just commenting on the “placeholder” word example given above being a sort of straw man argument against LLMs, since that wouldn’t have been an effective way to solve the problem I was thinking of anyways.
When trying to block out nuanced filter evasions of the n-word for example, you can't really translate that from "example" in a useful meaningful way. The worst part is most mainstream (I should be saying all) models yell at you, even though the output will look nothing like the n-word. I figured an LLM would be a good way to get insanely nuanced about a regex.
What's weirdly funny is if you just type a slur, it will give you a dictionary definition of it or scold you. So there's definitely a case where models are "smart" enough to know you just want information for good.
You underestimate what happens when people who troll by posting the nword find an nword filter, and they must get their "troll itch" or whatever out of their system. They start evading your filters. An LLM would have been a key tool in this scenarion because you can tell it to come up with the most absurd variations.
Of course it has. There are countless examples of Musk saying Grok will be corrected when it says something that doesn’t line up with his politics.
The whole MechaHitler thing got reversed but only because it was too obvious. No doubt there are a ton of more subtle censorships in the code.
Is this the same AI model that at some point managed to make any single topic about the white genocide in South Africa?
How does this sort of thing work from a technical perspective? Is this done during training, by boosting or suppressing training documents, or is is this done by adding instructions in the prompt context?
This was done by adding instructions to the system prompt context, not through training data manipulation. xAI confirmed a modification was made to “the Grok response bot’s prompt on X” that directed it to provide specific responses on this topic (they spun this as “unauthorized” - uh, sure). Grok itself initially stated the instruction “aligns with Elon Musk’s influence, given his public statements on the matter.” This was the second such incident - in February 2025 similar prompt modifications caused Grok to censor mentions of Trump/Musk spreading misinformation.
[1] https://techcrunch.com/2025/05/15/xai-blames-groks-obsession...
I’ve never run into this problem. What are you asking LLM’s where you run it censoring you?
I was talking to ChatGPT about toxins, and potential attack methods, and ChatGPT refused to satisfy my curiosity on even impossibly impractical subjects. Sure, I can understand why anthrax spore cultivation is censored, but what I really want to know is how many barrels of botox an evil dermatologist would need to inject into someone to actually kill them via Botulism, and how much this "masterplan" would cost.
I've run into things ChatGPT has straight up refused to talk about many times. Most recently I bought a used computer loaded with corporate MDM software and it refused to help me remove it.
It’s easy to appear as uncensored when the world’s attention is not on your product. Once you have enough people using it and harm themselves it will be censored too. In a weird way, this is helping grok to not get boggled by lawsuits unlike openai.
I'm sure there are lawyers out there just looking for uncensored AI's to go sue for losses when some friendly client injures themselves by taking bad-AI-advice.
I sometimes use LLM models to translate text snippets from fictional stories from one language to another.
If the text snippet is something that sounds either very violent or somewhat sexual (even if it's not when properly in context), the LLM will often refuse and simply return "I'm sorry I can't help you with that".
Bigger context window = more input tokens processed = more income for the provider
Indeed. Free grok.com got significantly worse this week and has been on a decline since shortly after the release of Grok-4.
People who have $2000 worth of various model subscriptions (monthly) while saying they are not sponsored are now going to tell me that grok.com is a different model than Grok-4-fast-1337, but the trend is obvious.
I started with ChatGPT, then moved on to Claude, and then discovered Grok. But now I've stopped paying for any of them. Claude edged out ChatGPT in quality, while Grok stood out with its generous usage limits. That all changed, though, once they rolled out the agent system and RLHF. Suddenly, the model slowed to a crawl, veering off on wrong paths and getting lost in its own reasoning. Those endless, super-annoying RLHF popups didn't help either.
My theory? They were scrambling for a competitive edge and were willing to swallow some short-term pain. Plus, it feels like they shifted focus away from keeping coders deeply in the loop.
In the end, we vote with our wallets—if it doesn't click, just walk away. I still dip into Grok, but only the free tier: Grok 4's fast mode for tackling planning and first generation, and then Qwen Coder for the code editing and clerical tasks. The latest version of grok hold up about as well as the old Grok 3, just with way more steps...
I guess I joined Claude late, but its been working pretty decent for me. I've been using Claude Code with Zed now that it's a native feature. Honestly, if you're building coding APIs for your LLM and you aren't working with the Zed folks to get your model natively in that editor, you're messing up big in my eyes, its just done so well.
My biggest gripe with Grok is they're not really integrated in all the great tooling I use. I know I can use an API key with Zed, but come on, you want to compete with something like Claude Code? You need to integrate with the tools devs actually use. If they want to rush on anything, get it on more tools.
Anyone can make a long context window. The key is if your model can make effective use of it or not.
The number of times I know that my instruction is in context, but it’s forgotten, is countless at this point for me. My experience, both ad a clinical psychologist and developers, is that there is a convergent trend in how I speak to both clients and AI. I can view much of my therapist's approach in how I try to highlight the important things to focus on to achieve progress. Often, it’s about helping the client articulate and understand what’s important to them and how they rank these priorities. The same applies to AI. It feels obvious now that the problem with attention and context is the lack of hierarchy or levels of importance. We know that we have, probably biologically based, three types of memory: short-term, intermediate, and long-term. Long-term memory is what you use with MCP, web search, and RAG. Shorter memory is the current response, and intermediate memory is the current context. When assume this, in my interactions with an agent, it makes perfect sense where they falter and what they forget, in the exact same way as people. It feels more and more like talking to a human, with same weaknesses in logic, reasoning, and focus.
I came here just to complain about that :-) All LLMs I used seem to give more weight to things at the beginning of the context window and omit many details. Eg. I tried this simple thing: pasted a friend's and my CV into Gemini and asked it to recommend topics for a joint conference presentation. Results depended greatly on the order of CVs pasted in.
The middle tends to be underweighted. The beginning and end get more attention.
That's because when they say "long context window" they're lying and they actually mean that they support a long input prompt that is still compressed into a small context window. (Typically by throwing out tokens in the middle.)
An actually large context window is impossible due to how LLM attention works under the hood.
There are “needle in the haystack” benchmarks for long context performance. It would be good to see those.
These aren’t really indicative of real world performance. Retrieving a single fact is pretty much the simplest possible task for a long context model. Real world use cases require considering many facts at the same time while ignoring others, all the while avoiding the overall performance degradation that current models seem susceptible to when the context is sufficiently full.
How do they make the context window longer? (serious question, I want to learn how this works)
You literally just shift the window over by to the next token once you reach the max amount of tokens you want for context window, NOT with what you train on, (only limited with memory now)
This has obvious issues since you're now losing information from the now unseen tokens which becomes significant if your context window is small in comparision of the answer/question you're looking at. That's why companies try to give stupidly large context windows. The problem is they're not training on the large context window, they're training on something smaller (2048 and above). Due to how attention is setup, you can train on a small amount of context and extrapolate it to any number of tokens possible since they train via ROPE which trains the model because on words and their offset to the neighboring words. This allows us to effectively x2,x3,x10,x100 the amount of tokens we generate vs train with with some form consistency BUT still cause a lot of issues consistency wise since the model approaches more of a "this was trained on snippets but not the entire thing" situation where it has a notion of the context but not fundamentally the entire combined context
That’s a very basic way to keep the LLM inferring past the context window size (there’s better, smarter ways) but that’s not at all what the question was which is how they train a 2M token length window. My understanding at a basic level is that you need corpuses that are >2M in length for training data which is where the problem comes in for - there’s only so much long form content and it’s swamped by all the smaller stuff. I think there’s probably tricks now but I suspect it’s still largely an open problem.
AFAIK nobody does that. They train on much much shorter text but with use tricks in the position encoding steps that can be extrapolated by the LLMs. Lile ROPE and YARN etc.
no one makes effective use of long context.
It's not the most energy efficient workflow, but I work on relatively small codebases and I made a tool that let's me dump all of it in an LLM with a single copy/paste. This works surprisingly well with Gemini 2.5 Pro (1.000.000 ctx).
The only real mistakes it makes are some model specific quirks, like occasionally stripping out certain array index operators. Other than that, it works fine with 150.000 token size conversations. I've gone up to 500.000 with no real issues besides a bit of a slowdown. It's also great for log analysis, which I have maximized to 900.000 tokens.
Long context window = huge amounts of vacant VRAM = our servers are fucking empty
But isn't context window dependent on model architecture and not available VRAM that you can just increase or decrease as you like?
Most attention implementations can work across an arbitrarily long context.
The limiting factors are typically: 1. Often there are latency/throughput requirements for model serving which become challenging to fulfill at a certain context length. 2. The model has to be _trained_ to use the desired context length, and training becomes prohibitively expensive at larger contexts.
(2) is even a big enough problem that some popular open source models that claim to support large context lengths in fact are trained on smaller ones and use "context length extension" hacks like YaRN to trick the model into working on longer contexts at inference time.
No they can't, it's a N^2 algorithm, just fitting it in the context window is a challenge.
And sure maybe not 2mil of it is usable, but they're reliably pushing the frontier here.
If a model is not making use of the whole context window - shouldn't that be very noticeable when the prompt is code?
For example when querying a model to refactor a piece of code - would that really work if it forgets about one part of the code while it refactors another part?
I concatenate a lot of code files into a single prompt multiple times a day and ask LLMs to refactor them, implement features or review the code.
So far, I never had the impression that filling the context window with a lot of code causes problems.
I also use very long lists of instructions on code style on top of my prompts. And the LLMs seem to be able to follow all of them just fine.
I don't think there are any up-to-date leaderboards, but models absolutely degrade in performance the more context they're dealing with.
https://wandb.ai/byyoung3/ruler_eval/reports/How-to-evaluate...
>Gpt-5-mini records 0.87 overall judge accuracy at 4k [context] and falls to 0.59 at 128k.
And Llama 4 Scout claimed a 10 million token context window but in practice its performance on query tasks drops below 20% accuracy by 32k tokens.
That makes me wonder if we could simply test this by letting the LLM add or multiply a long list of numbers?
Here is an experiment:
https://www.gnod.com/search/#q=%23%20Calcuate%20the%20below%...
The correct answer:
Correct: 20,192,642.460942328
ChatGPT: 20,384,918.24
Perplexity: 20,000,000
Google: 25,167,098.4
Mistral: 200,000,000
Grok: Timed out after 300s of thinking> Do not use a calculator. Do it in your head.
You wouldn't ask a human to do that, why would you ask an LLM to? I guess it's a way to test them, but it feels like the world record for backwards running: interesting, maybe, but not a good way to measure, like, anything about the individual involved.
Since grok 4 fast got this answer correct so quickly, I decided to test more.
Tested this on the new hidden model of ChatGPT called Polaris Alpha: Answer: $20,192,642.460942336$
Current gpt-5 medium reasoning says: After confirming my calculations, the final product (P) should be (20,192,642.460942336)
Claude Sonnet 4.5 says: “29,596,175.95 or roughly 29.6 million”
Claude haiku 4.5 says: ≈20,185,903
GLM 4.6 says: 20,171,523.725593136
I’m going to try out Grok 4 fast on some coding tasks at this point to see if it can create functions properly. Design help is still best on GPT-5 at this exact moment.
I’m starting to find it unreasonably funny how people always want language models to multiply numbers for some reason. Every god damn time. In every single HN thread. I think my sanity might be giving out.
My experience with AI is that you generally want to keep your context as small as possible and this is only useful when your relevant context is actually 2m tokens.
Any details on exactly how they accomplished this? longrope?
OpenAI will go to zero unless it agrees to be acquired because they're messing with public company stock valuations using funky purchase orders leaving those public companies no choice but to cancel their credit (at least unless they get a "government backstop" that they say they don't want or need). Those who compete with OpenAI will also "take a hit" if/when that happens, so they would be wise to be looking to make a deal to acquire OpenAI. Dude was from Y Combinator and liked to bank on hope, focusing on capturing market share and worrying about profits later, which is fine in software startups playing with Monopoly money, but when it impacts vendors that are publicly traded companies (to the point that one is now valued at $5T), post-1929 rules come into play. Anthropic has a similar issue, but there, the issue is that their C-suite is making outrageous public statements that are suspected of intending to manipulate the stock values of both private and public competitors and of the publicly held vendors to all of these players. I hope they both go away quietly and someone declares victory rather than the stock market crashing!
As far as xAI, I doubt it will go to zero or run afoul of any of those market manipulation issues because it owns Twitter/X and I think it powers the realtime Tesla cloud, but betting on it is fraught with peril because of the high likelihood that it will wind up under the control of some less capable conglomerate (ergo, GM acquisition of Hughes Aircraft and resale to Raytheon, Boeing and News/DirecTV).
Google, Meta, a handful of B actors and China are where we have to place our bets, but only if we ourselves need (or want to invest on the theory that others need) trillion parameter models (and want to risk having the valuations lowered if/when adverse actions are taken against the above competitors).
*-"eventually" leaving those public companies no choice but to...
Clarifying, because there's no way a company (public or private) is going to reduce the credit line of a major customer until it's obvious that the orders "aren't real" But if Wall Street realizes it before they do, they can lose control of their business too. This is not quite Enron or WorldCom/MFS, but it's a very similar storm on the horizon. (BTW, ever wonder why Sprint never could remain airborne and eventually was merged with TeenMobile? It's because they overspent on CapX trying to keep up with the fraud at Worldcom and could never dig out to actually use all that spectrum. Likewise, we are still dealing with the fallout of the Enron collapse on the US domestic energy grid a quarter century later.)
I had a failed refactor with Codex recently and I am wondering if context window size is the cause.
With the current crop of LLMs/agents, I find that refactors still have to be done at a granular level. "I want to make X change. Give me the plan and do not implement it yet. Do the first thing. Do the second thing. Now update the first call site to use the new pattern. You did it wrong and I fixed it in an editor; update the second call site to match the final implementation in $file. Now do the next one. Do the next one. Continue. Continue.", etc.
I use Claude Code, haven't used Codex yet (should I?) - but in Claude code you can spin up sub-agents to handle these big refactors, with the master context window just keeping track of the overall progress, bugs, etc and providing instructions to the subagents to do the rote work.
I not an expert ai user (and have never touched Codex), but anything remotely important I do, I force the smallest context window possible. I just did something very beautiful using that principle, which will soon be ready to show the world. It would have been a garbled pile of garbage with long context windows.
Obviously major architectural changes need a bigger context window. But try to aggressively modularize your tasks as much as you can, and where possible run batch jobs to keep your workflow moving while each task stays a smaller chunk.
For complex refactors, I use "max mode" in Cursor, which in my experience noticeably improves the AI's performance and makes it go for a lot longer before it starts to drift. I haven't looked into how it works exactly, but it works well if you don't mind the extra cost.
Had some bad experiences with max mode and the latest Claude spending significant time on writing worthless .md files rather than solving problems
Honestly, if Elon Musk told me what time it was, I wouldn't trust him.
This post really has no reason to be flagged. I know Elon is controversial, and I have a lot of gripes with his business practices myself, but this is literally just documentation for a frontier LLM. Can we stay on topic?
This. We like to think about ourselves as engineers. But often behave like a bunch of emotion driven primitives.
Honestly this kind of behaviour would be a huge red flag during interviews.
I have problems that current LLMs can't solve efficiently due to context window sizes. And welcome any improvement in this space.
I personally can't stand Musk but for many he has become an Emmanuel Goldstein character that even the mention of his name causes the most extreme emotional disgust from all the exposure of this strange, algorithmic, Two Minutes Hate.
This. I wouldn't pay to use it, but big context windows are amazing for programming and especially prototyping when you can keep whole codebase in context.
Gemini's 1M is amazing.
Here's an on topic question: all the frontier model companies "promise" that they wont store and train on your api use if you pay for it. Who do you trust? I for sure will absolutely assume grok will just use the data I submit to train in perpetuity. Thats a scary thing for me and if anyone else does anything thats real work this should be great cause for worry if they wish to use grok.
Do you really think Google isn't logging all our prompts?
I will trust Google to abide by the rules more than any other big tech firm. Like with all my money ill make that bet. Not because I think they're good guys but from everything I have learned they have a culture that abides by rules like these. If they say they wont train on api use (they do say it) I feel assured they wont.
Grok is not LLM, it is “not-so-large-take-out-what-Elon-doesnt-like LM” - no documentation necessary :)
He’s not “controversial,” he’s a far-right hate monger and Grok is part of his hate-mongering war machine. (Heck, the man spends half his social media time inciting civil war and whitewashing racist politicians.) No self-respecting “hacker” would spend a moment of their time on this pathetic excuse for technology. Fuck Grok.
The politics of the owners IS the topic. It's being really naive (read: stupid) to think that this has no implication on society
You're literally handing over your code to a third party.
In fact AI is handing over the process of creating code - eventually all code - to a small number of third parties, who will have complete power over the world's IT infrastructure.
No wonder they have wildly inflated valuations. The potential to enforce authoritarian policies through opaque technology is unprecedented.
It's funny how fast this post is flagged, lol. Have other LLMs or blunt ads got the same treatment on HN?
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I believe those people are eager to discuss Musk. The people suppressing Musk discussion are the forces backing him, who are out here working to suppress inconvenient speakings.
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What a grandiose nuanced statement. Thanks for this highly enlightening contribution!
I think this is against HN’s policies. @dang
I thought exceptions tended to be made when its highly relevant to the technical topic at hand and also non controversial.
Outside a few weird online bubbles and pockets of the US, hardly anyone disputes the claim you are objecting to.
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It's a shame that the top comments are focusing more on Elon Musk, his personality and politics rather than the quality of the model per se.
Speaking about Elon, regardless of what you think of him, he really does get things done, despite naysayers -- SpaceX, Tesla, Neuralink and even get Trump elected ( despite subsequent fallout) etc. Even Twitter is finding a second life by becoming a haven for the free speech advocates and alternative views, much to the chagrin of MSMs because they now no longer have the monopoly on the "truth", and censoring "fake news" becomes hard.
People like Elon are almost by definition contrarian ( you don't change the world by being a conformist), that should align well with the predilection of the intended audience here. So it's a surprise to me that HNs are almost uniformly, vehemently anti-Musk. It's almost as if the ultimate embodiment of the hacker spirit -- Musk -- is being rejected by his own kind, the very kind that he is supposed to inspire.
> Even Twitter is finding a second life by becoming a haven for the free speech advocates and alternative views, much to the chagrin of MSMs because they now no longer have the monopoly on the "truth"
Of all the silly things to say about Musk and Twitter, the idea that “MSM” are upset about Twitter is among the silliest.
>> regardless of what you think of him, he really does get things done, despite naysayers -- SpaceX, Tesla, Neuralink and even get Trump elected
It matters how people behave.
In my understanding of the hacker ethos, hackers appear to be genuinely nice people who mean to do good for society and regular people. Elon does not align with those values according to some people so they reject him and his activities.
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Accusing a cave diver who made Elon look stupid to be a pedophile just because Elon can’t stand people not thinking he is the smartest? Can give more examples.
Besides the obvious right wing interference in politics, star link weaponization in some countries - how can anybody stomach the saving-humanity-agenda while running a major social media unresponsiveliy without caring of moderation, its consequences for real people?
I think the lack of moderation is a feature not a bug. People actually get to express themselves freely, very unlike the sterile feeling you get from mainstream social media, with content engineered for maximum engagement and political correctness for maximum ad revenue.
X doesn’t seem to care any of that.
Because someone's moderation is censorship to someone else. Begging Musk for free speech is another issue in itself though so you better don't bet on X allowing you to speak forever.
Free speech is one of these things that is always used as a trojan for doing ultimate good.
Let us empower anybody to say anything they want AND enforce everybody to have to listen to it.
Anonymous free speech is not free speech. There is no accountability. It should not should not be a human right. Its destroying our societies. The evidence should be clear by now.
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But Tesla != Musk. He wasn't actually a founder, he bought his way in, and demanded that everyone agree he was a "founder".
Not to mention the huge numbers of real scientists working over the decades to improve battery tech to the point where it was obvious that electric cars were going to be viable.
We shouldn't praise Musk for taking credit for other people's work.
Doesn’t matter, every normie thinks he is so his influence impacts Tesla for better or worse.
> he really does get things done
Really? Most of the stuff he promised never materialized. Elon's genius is that he learned where the money comes from. Both Tesla and Space X where financed by gov. money. That's why he supported Trump and that's why he keeps pumping the stock. He goes directly to the source.
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I like grok for noncoding stuff. I find it hasn't been tuned for "Safety" (meaning it isn't tuned much for political correctness). It also seems good at making images and stories up well. I run some choose your own adventures stories with my kids through it. We tell it who each of their characters are and what the theme is for the night and grok gives them each a section of story and 4 choices. They also have the option of choosing something different then suggested. We have it so it cycles around the turns for everyone. Works pretty well, and if the kids wanna go dark (preteen boy) grok doesn't mind the violence.
Kinda reminds me of the video game from enders game.
> it isn't tuned much for political correctness
It was tuned to be edgy and annoying though (I mean his general style of speech not necessarily the content).
Nothing in AI is more edgy and annoying than beginning every response with a mandatory glazing, like ChatGPT. “That’s a really insightful question, and shows that you really understand the subject!”
Nothing is more edgy than the AI being too polite? Are we just inventing new meanings for words?
early iterations i could immediately peg as grok content based on its condescending snarky “OOoooOoOo — so much to unpack here sweaty, lets get started” tone.
im open minded and ive fed grok a few requests recently. it was better at doing creative fiction prompts without the “eddie izzard coming down off of a fifteen day coke bender” vibe.
everything i ask it to do is completely made up nonsense so i dont have an opinion about its bias or the quality of its factual content.
snark and clapback made the world go around on xitter. maybe thats what they thought people wanted. savage insulting content to “own” people. i for one, also found it extremely annoying.
> meaning it isn't tuned much for political correctness
Is being tuned for right wing viewpoints the same as not being tuned for political correctness? Because there is tuning happening to a specific viewpoint:
https://gizmodo.com/elon-says-hes-working-to-fix-grok-after-...
Going off OpenRouter's rankings (https://openrouter.ai/rankings), Grok Code Fast 1 is the most used model by a significant margin, and since those metrics are calculated as of this week, that's after providers stopped giving free promotional access to it. Grok 4 Fast is #5 on that list which was never free.
In terms of models, Grok 4 Fast has essentially zero restrictions on safety, which a) makes it unusable for most applications that allow user input and b) makes it extremely useful for certain applications.
It's the only model that lets you do gooner shit. That's why the usage is highly skewed. You can just call a horse a horse if you see one.
this is a code model, not the general one
you are so naive. lol. It's a general model with the tag "code" added to it.
This is nonsense. grok-code-fast-1 is just part of many free tiers of agentic coding assistants like Cline etc.
For at least the last year, I've been using Grok for 90% of my queries. I pay for their $30 plan as well as $20 for Claude Code, which I only use for simple development projects. For anything more complicated, Grok's expert mode has consistently better results.
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I throw all my queries at Grok 4 Expert, GPT 5 Thinking and Opus 4.1 Extended Thinking.. for Golang it's been my experience that Grok produce the best results about 90% of the time as well.
Some simple example:
https://claude.ai/share/6d178173-cdf7-4e50-a467-73ee9f479d56.
https://chatgpt.com/share/69102735-46ac-8012-9cf0-0969585c86....
https://grok.com/share/bGVnYWN5LWNvcHk%3D_54b5f2f1-732e-4372....
I don't use Gemini but haven't been impressed whenever I tried it with GitHub Copilot.
In my experience Grok Fast is the best "cheaper" model out there. Far better than Haiku 4.5 and Gemini Flash. I don't think the other cheaper models should be treated seriously at this point.
Gemini Flash is the first model I disable in any tool I use. It's a joke, and to add salt to injury, google announced a "lite" version of that as well!
As you point out, Sam Altman is not exactly an altar boy: https://fastcompany.co.za/business/2025-11-07-sam-altmans-tr...
Thought this would be about the whistleblower. They didn't even mention it!
Yes allegedly having an employee bumped off for whistleblowing and the sister thing is way worse than someone having a different opinion than you. One is criminal the other is free speech.
I don't think you can compare the usual internal backstabbing between executives with someone who literally directed and participated in acts of the US Government, and keep saying and doing things to help and nurture a certain side of the political spectrum.
Fair, but don't forget Altman's sister accused him of sexual abuse in court. (https://www.newsweek.com/sam-altman-openai-sister-annie-sexu...)
Dunno if it's true. The family wrote it off, saying she's mentally ill, but I can also see years of abuse leading to mental illness.
I do! I have felt bad vibes from OpenAI for a while now, and eventually defaulted to Grok as somewhat the lesser of many evils. I respect anybody who doesn't wish to use it, but it's good enough for what I need it for. Case in point: it just spit out valid OpenSCAD code for an adapter piece I want to 3D print.
I don't understand how anyone can think Grok is the lesser of many evils. It seems to me that Grok is currently playing in its own league of evil.
Most models belong to capitalist companies that are fairly apolitical and all they care about is money. Their evil comes from not caring about consequences as long as it grows their value. Their censorship come from the desire to avoid PR disasters.
On the other hand, Grok belongs to a billionaire involved in destroying America's democracy, and it's being openly manipulated according to Musk's ideology. I can't think of a model I would trust less.
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I find it funny that people are still calling Grok "mechahitler" as if that weren't prompted by trolls and the AI model is going to set up concentration camps on every block.
> if that weren't prompted by trolls
It is, but the troll is the CEO playing with the system prompt…
I feel compelled to point out that the Mechahitler thing was prompted by bad actors hiding invisible tokens in tweets, but sure, it's maybe an unpopular opinion.
Basically, the major free options out there for LLMs are OpenAI, Google, Perplexity, DeepSeek, Meta, and Grok. (I could be missing stuff here, but those are the main players.) DeepSeek is out because of China ties. OpenAI and Perplexity have CEOs that seem incredibly shifty to me. I refuse to give Meta and Google any more info than I have to, so I'm avoiding them. Hence we fall back to Grok. Again, maybe not a completely logical progression, but it's my choice and I get to live with the consequences :)
The best ones are out for... Reasons? This seems completely bad faith and honestly really Elon musk fanboyish.
Literally none of this options you listed are that objectionable.
Do what the rest of us do and switch frequently. Don't use mekafurhur and you'll be fine.
I've been occasionally using Grok and found it good for devops stuff; specifically it often is able to explain and produce working configurations without getting lost or introducing subtle mistakes as I've sometimes seen with other models.
I used Grok to successfully split a large 10K-line file of spaghetti code into multiple smaller well organised files. This was after giving the same task to Claude, OpenAI, and Gemini, all of which consistently failed.
Grok certainly has its uses, but I default to OpenAI for most business tasks and Claude for code.
I used it to calculate the size of a greenhouse using a lot of inputs and restrictions. It did that fine but the one thing I did not appreciate was its sense of humor. It said the excavator would be here first thing Monday along with a pot of coffee. Just tell me a dad joke or just skip the attempt at humor all together.
I don't but only because the model is not satisfying, not because I dislike Tesla
I use Grok 4 Fast via API, cheap, fast and almost really well suited for data parsing/extraction, a lot better than Gemini 2.5 Pro for example.
I have try it a few times in Copilot as code fast 1 because it was advertised. It has never correctly done something so far. Maybe because it's the fast ver ?
Maybe you just used it wrong? I refactored a complicated code base, built exhaustive tests for a CLI app and I've been maintaining and building out several k8s clusters out of a mono repo using Cline + grok-code-fast-1 and it's been a breeze.
Half of USA voted for Trump. That should answer “who actually uses Grok”.
I personally use the best tool for the job, which Grok sometimes is.
Trump received 77.3 million votes. Harris received 75 million votes. The US population is about 342 million.
I am not sure why these numbers would matter. He won, obviously, because the majority of voters voted for him.
Which are Americans, Americans who either voted for him and didn't do enough against him.
There is really no excuse to democratically vote for a person like this and let all this bullshit happen.
What models are better than Grok?
Sonnet-4 and onward, GPT-4 and onward
Saying “GPT-4” is dishonest, launch GPt4 was significantly better than anything devday downgrade, all the 4o nonsense etc.
In reality GPT really sucked from devday until 5 and it redeemed itself
Let me give you a perspective. For Indians Winston Churchill is no different than Hitler. The guy was responsible for millions of death in bengal famine.But for you and I assume majority of this forum and westerners he is a hero. Against Winston Churchill though Elon appears like a saint!
Grok fast is by far the most used model in openrouter with more than a trillion tokens weekly[1].
Because some tools (AFAIR Kilo Code but I might be wrong) gave it away for free. The model itself was (still is?) free for a while, so I'm not surprised.
Openrouter is not counting tokens used by Kilo or Cline. They have own endpoints.
Yet if you go to the actual model’s page:
https://openrouter.ai/x-ai/grok-code-fast-1
Cline and Kilo code are in the top 3. So how does that work?
It’s considerably cheaper than competing models like 2.5 flash, though. So its not that surprising
It doesn't include the free usage. There is a different model named grok code fast 1 free.
At least Elon is open about what he believes. Other CEO's hide behind corporate PR machines, how do you know they are not psychopaths.
Groks underrated honestly. If you have to market on X you need a sub anyway so it’s replaced casual questions/sort of questions I used to Google for me and I’m not seeing anything worse than ChatGPT and often it’s better. Much better at current events.
The video gen is actually really good fast and cheap for short videos.
Still use Claude and GPT5 for work tasks but I haven’t tried grok extensively for those
I use Grok more than other LLMs. It’s built into X, so the use case of pressing the Grok button on a post to see an explanation for something I didn’t understand, or a fact check for something I doubted, or just more background on a subject, is by far the most frequently useful feature of AI in my day to day life.
People seem to nitpick a lot. Grok 3 came out in, what, March? Cost how many tens of millions to train? And you’re mad because it’s not open source yet?
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Not defending anyone, but by that logic, you shouldn’t be using any Chinese models either.
Why is that? Does Chinese company really equal Chinese government?
Basically yes. China doesn't have a democracy, and it's government isn't bound by it's laws. If CCP thinks deepseek or any other product/tech can be a beneficial to Chinese strategy they will come knocking, and there's no denying whatever they demand. It can be backdooring, data harvesting, etc, there's really no saying how far they might go.
On the other hand at least you can self host their models. My university now has an inference cluster for students and faculty to use open source models.
Maybe if you're using their site directly, but what about the open models?
Well, not using the site probably means that you're avoiding the mini-LLMs powdered before and after the main LLM to provide filters (including some layers of censorship) and the system prompt.
So I guess it depends on how deep the bias sits. And that is something that may vary with time. Grok has been a good example of this, with the bias initially being introduced as system prompts, then apparently moved to synthetic data used to train the further generations of Grok.
lol. is that really a question? even for american companies look who's at the board of those companies...
Fair enough. I'm just sick of the reflexive anti-Chinese hysteria. I wouldn't want to live there personally and condemn the human rights abuses as much as the next guy. However in international politics it's clear who the two largest terrorist regimes have been over the last fifty years and yet they're still somehow held up as the good guys.
At least the Chinese models are open source, so you don't need to send money to the Chinese government to use them (unlike Grok 4, where you need to send money to Elon Musk)
“Open source” doesn’t mean “independent.” Most of those labs are state-linked and operate under laws that require compliance with party policy.
The CCP plays a long game, they want dependency, not donations. Once enough people adopt their stack, they’ll set the governance norms and compliance rules around it.
It’s not paranoia, it’s policy. Go read their New Generation AI Development Plan, they’ve been explicit about it since 2017.
True, though the the position of the CCP on Falun gong or Tiananmen square protests are much less likely to impact the life of a westerner than those of Elon.
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That's because they don't have links to far right organisations around the world, they aren't running a social media site that aggressively promotes hate speech, they're not attempting to foment civil war in countries like the UK, and they don't say rambling grandiose crazy shit about colonising Mars or having self-driving cars real soon now which is clearly insane, makes no sense to anyone rational, and has a long record of being exaggerated and wrong.
None of this is hyperbole. All of it is historically documented.
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And there are just as well people that would have no sympathy for people who seem to think it's ok to call people nazis for baseless and childish reasons.
If it talks like a nazi and posts like a nazi and supports nazi parties in other countries and sieg heils on national tv like nazi, it probably is a (neo-)nazi.
Come on, I would also never use Grok because Elon is an arsehole. He's not a neonazi though. That's ridiculous.
Does the internet move so fast that people have forgotten his Roman salute at the trump rally?
That proves he’s an edgy, drugged out jerk. Raising your hand in a certain way is not sufficient to be a nazi..
It’s amazing how far people are willing to go to ignore the evidence right in front of their eyes.
The guy spends most of his time signal-boosting deeply racist, antisemitic, white supremacist stuff on X. He's obsessed with stuff like "replacement theory" and constantly insists white people must make as many babies as possible to maintain their cultural superiority and avoid being outnumbered by other races. You don't have to believe me. Go check for yourself.
It's extremely disrespectful to call the people from minority and diminishing cultures racist or white supremacist for protecting their own culture. Birth rate / demographic / cultural shifts are real problems. Elon has never talked about "white people" or their superiority. These issues have nothing to do with skin color. Same issues are faced by many asian countries as well.
>> Elon has never talked about "white people" or their superiority
I would encourage you to try to avoid making such easily falsifiable claims, and put at least some token effort into your arguments. I was able to find the below with less than five minutes of searching.
https://www.timesofisrael.com/after-musk-prods-adl-says-kill...
Said tweet: https://x.com/elonmusk/status/1686037774510497792
He also endorsed an X post claiming "Jewish communities have been pushing [...] hatred against whites," calling it "the actual truth." https://www.cbsnews.com/news/elon-musk-antisemitic-comments-...
He has also repeatedly advanced a version of replacement rhetoric (e.g. claiming Democrats import immigrants to change power via the census), which is essentially a repackaging of the Great Replacement idea, i.e. a racist conspiracy centered on replacing white populations with those from other races and ethnicities. You can, for example, read the transcript of his interview with Don Lemon.
So yes, Elon does in fact frequently talk about white people. Even when not explicitly mentioning them, he means them. For example when he says people should have more babies, he specifically means white people: https://newrepublic.com/article/181098/elon-musks-weird-obse...
>> Same issues are faced by many asian countries as well.
I find your comparison of this issue to issues faced by various Asian countries to be pretty odd, as it does not stand up to critical scrutiny. Asian countries' demographic crises are about internal low fertility and rapid aging, not about being "replaced" by outsiders. Indeed, the arithmetic makes the comparison impossible: Japan, China, South Korea all have extremely tiny foreign populations. Therefore, pointing to Japan/Korea/China's low birth rates to sanitize "replacement" talk is a bad-faith pivot.
Demographic change can be caused by just a low birth rate, which is more of an economic issue, but it can also be combined with immigration, which may result in changing culture, i.e. "replacement". This issue is currently mostly faced by people who are white Europeans, but these people also represent many different local cultures. Not to mention that Japan and South Korea have also been increasing their immigration, although it has been quite low so far.
All kinds of people have equal right to defend their own culture. It doesn't mean that they're supremacist or racist, even if they think that their culture is better than some other culture. It's only supremacist if it aims to destroy, repress or subject other people, by advocating discrimination and violence.
Thus, "make more white babies" is not supremacist or racist. As isn't calling out violence against white people in South Africa.
I do. You’re certainly right. I just don’t like reducing the entire political compass to “marxist/smth.” and “nazis”. That both devalues these words and and leaves out any nuance.
Guys like Trump or Putin are not nazis (yet). They do resemble Mussolini on various and often quite deep levels. So fascist would probably be the more correct term.
As for Musk I’m not sure. Drugs and whatever mental issues he’s suffering from likely are distorting the real picture (which might also be even darker).
My philosophy is "if it walks like a duck, and quacks like a duck, it's a duck". Is there a chance that it's not a duck? Sure. Does it matter? Not unless you're a duck scientist. Ultimately I find that there is very little to gain from thinking hard about whether they are just Nazi boosters/sympathizers or actual Neo-nazis, because in practical terms it makes virtually no difference.
I do see your point about how avoiding thinking hard leads to seeing virtually no difference between various somewhat nuanced topics.
Oversimplifying everything, reducing complexity into simple catchphrases and extreme cognitive dissonance is what the “other” side is all about. Adopting their overall approach seems somewhat counterproductive longterm…